Machine Learning Engineer Personalization & Recommendations

Imagine leveraging your expertise in recommendation systems and data-driven personalization to directly improve the lives of millions of people living with hypertension. Hilo by Aktiia, is revolutionizing how cardiovascular health is monitored and managed. They’ve developed and commercialized the world’s first continuous blood pressure monitor, empowering both patients and physicians with unprecedented insights into blood pressure patterns. Backed by over $100M in funding from top-tier investors across Europe and the US, Hilo is an early-stage, fast-growing startup rooted in over 20 years of research at the prestigious Swiss Center for Electronics and Microtechnology (CSEM). Their CE-certified, Class IIa medical device is already available in over 10 countries worldwide—and they’re just getting started. As Aktiia enters a new growth phase, we’re looking for a hands-on and pioneering Machine Learning Engineer to build the foundation of their new recommendation and personalization layer — shaping a capability that will sit at the heart of the product innovation. As Machine Learning Engineer focused on “Personalization & Recommendations”, you will design and build the application layer that powers this next-generation engine — from user segmentation and clustering to insight generation and personalized, behavior-driven recommendations. This is a hands-on role for someone eager to create something new from the ground up. Your Role Application Layer Development Design and build the foundation of Aktiia’s next-generation recommendation engine together with a cross-functional software team (~20 engineers). Dynamic User Modeling Develop evolving user profiles that adapt continuously as new data and interactions are integrated. Insight Generation Transform raw signals into clear, personalized, and engaging user-facing experiences. Cross-Domain Adaptation Leverage proven methods from recommendation-heavy domains (e.g. travel, streaming, commerce) and adapt them to digital health. End-to-End Ownership Drive projects from prototyping and experimentation through testing, deployment, and iteration in production. Collaboration Work closely with product, data science, and engineering teams to ensure solutions are robust, user-friendly, and aligned with business goals. Pioneering Role Help Aktiia apply recommender system principles in a new domain, shaping something that has not been built before. Your Profile Academic Background PhD or MSc in Informatics, Mathematics, Physics, Biology,Data Science, Signal Processing or related field. Professional Expertise 4+ years of experience building and deploying recommendation systems in production, with expertise in personalization, clustering, and recommendation logic. Proven track record generating user-facing insights and iterating recommendation models at scale. Knowledge of reinforcement learning and its application to personalization and recommendation systems. Technical Experience Strong programming skills in Python with hands-on experience in modern ML/AI frameworks. Skilled in building, validating and deploying ML models. Familiar with data pipelines, cloud and best engineering practices. Experience with large language models (LLMs), including practical use of open-source models, RAG, embeddings, and fine-tuning. Industry Fit Preferred background in recommendation-driven industries such as travel, streaming, e-commerce, advertising or digital marketing. Ideally from start-ups or mid-sized companies with end-to-end system ownership. Language Skills English at highly proficient level is a must. French, German or Italian are advantageous. Personality Hands-on, execution-driven product builder with strong ownership, accountability, and customer focus. On top you are a strong team player! Do you want to build something that changes lives while advancing science and technology? Then we’re excited to meet you! jid5cf4b2ba jit0939a jiy25a